Lead Machine Learning Engineer at Relevance AI

Full-time, Software Development, Sydney, AU sydney engineering full-time
Description
Posted 6 months ago

At Relevance AI our mission is to accelerate developers to solve similarity and relevance problems through data, this enable important use cases such as recommendations, topic modelling, semantic search, zero-shot classification and more.

Our first step towards helping teams solve similarity and relevance, we started with the data type that all the top tech companies use - Vectors, a high dimensional representation of data used to determine similarities between data, commonly produced through deep learning. 

We are looking for a Lead Machine Learning Engineer to develop and expand on our cutting-edge vector platform. You will be joining a rapidly growing team backed by Insight Partners (investor in Monday.com, Twitter, etc) where new ideas and state of the art Machine Learning is applied daily.

Responsibilities:

  • Design accurate and scalable algorithms for creating, storing, evaluating, searching or analysing vectors/deep learning embeddings

  • Analyze and preprocess raw data: assessing quality, cleansing, structuring for downstream processing

  • Collaborate with engineering team to bring your research and prototypes to production

  • Be involved in all aspects of a project life cycle, work with clients to illustrate and integrate Vector AI to generate real business value for them

  • Self starter, take ownership of their work and the quality of it.

Qualifications:

  • Degree or equivalent experience in quantative field (Statistics, Mathematics, Computer Science, Engineering, etc.)

  • At least 5 years of hands-on experience in using Python for Data Science with projects and outcomes to show for it

  • Understanding of Vectors/Deep Learning embeddings and have experience in utilising them for search, recommendations, personalisation, etc

  • Deep understanding of training Deep Learning models in either Pytorch or Tensorflow (including Convolutional Neural Networks, LSTM, Transformers, Autoencoders, etc)

  • Deep understanding of traditional statistical modeling: clustering, dimensionality reduction, K-nearest neighbors

Bonus Qualificaitons:

  • Familiarity or Experience with Docker, Kubernetes, Kafka, Spark, Elasticsearch, MongoDB, Lucene, SQL or Plotly

  • Familiarity or Experience with python libraries of: FastAPI, FAISS, RAPIDS, nmslib, Dask

  • Speciality in a specific field of machine learning: Computer Vision, Time Series, Natural Language Processing, Audio, Clustering, etc

  • Strong familiarity with a certain industry where vectors are or can be applied to.

Apply now to be an early journey of a startup that will empower data science and developers with the tooling they deserve.